3D Point Cloud Classification Algorithm Based on Residual Edge Convolution
DU Zijin1, CAO Feilong1, YE Hailiang1, LIANG Jiye2
1. College of Sciences, China Jiliang University, Hangzhou 310018 2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006
Abstract：The irregularity and disorder of 3D point cloud make the classification of point cloud more challenging. Therefore, a 3D point cloud classification algorithm based on residual edge convolution is designed. The discriminative shape descriptor can be learned from the point cloud directly and used for target classification. Firstly, an edge convolution block with residual learning is designed for feature extraction on the point cloud. In the edge convolution block, local graph is constructed with the input point cloud through the K-nearest neighbor algorithm and the local features are extracted and aggregated via convolution and maximum pooling, respectively. Subsequently, global features are extracted from the original point features through the multi-layer perceptron and combined with the local features in a residual learning way. Finally, a deep neural convolution network is constructed with the convolution block regarded as the basic unit to realize the classification of 3D point cloud. The organic combination of local features and global features is considered comprehensively. With a deeper structure, the network makes the final shape descriptor more abstract and discriminative. Experiments on two challenging datasets, ModelNet40 and ScanObjectNN, show that the proposed method obtains superior classification results.
杜子金, 曹飞龙, 叶海良, 梁吉业. 基于残差边卷积的3D点云分类算法[J]. 模式识别与人工智能, 2021, 34(9): 836-843.
DU Zijin, CAO Feilong, YE Hailiang, LIANG Jiye. 3D Point Cloud Classification Algorithm Based on Residual Edge Convolution. , 2021, 34(9): 836-843.
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